class: center, middle, inverse, title-slide .title[ # Class Session Slides <br> 🐱 ] .author[ ### S. Mason Garrison ] --- layout: true <div class="my-footer"> <span> <a href="https://DataScience4Psych.github.io/DataScience4Psych/" target="_blank">Data Science for Psychologists</a> </span> </div> --- ## Navigation Menu ### **Modules Overview** - [M1: Welcome to Data Science](#3) - [M2: Data and Visualization](#4) - [M3: Welcome to the Tidyverse](#5) - [M4: Data Diving with Types](#6) - [M5: Effective Data Visualization](#7) - [M6: Confounding and Communication](#8) - [M7: Web Scraping](#9) --- ## **Module 1: Welcome to Data Science** ### **Today’s Plan:** - **Introduction to Data Science**: Key concepts and importance. - **Tools & Setup**: Overview of R, RStudio, and Git. - **Exploring Data**: Initial data exploration techniques. - **Hello R**: Hands-on lab (for those at that stage). **By the end of this module, you should be able to:** - Explain what data science is and why it matters. - Set up and use basic tools for data science. - Perform basic data exploration using R. [Back to Menu](#2) --- ## **Module 2: Data and Visualization** ### **Today’s Plan:** - **Exploratory Data Analysis (EDA)**: Understanding data distribution. - **Principles of Visualization**: Designing clear and effective graphs. - **Using `ggplot2`**: Building visualizations step-by-step. - **Plastic Waste**: Hands-on lab (for those at that stage). **By the end of this module, you should be able to:** - Conduct exploratory data analysis in R. - Design effective and clear data visualizations. - Use `ggplot2` for creating different types of plots. [Back to Menu](#2) --- ## **Module 3: Welcome to the Tidyverse** ### **Today’s Plan:** - **Tidy Data**: Understanding structured data principles. - **Grammar of Data Wrangling**: Key transformations with `dplyr`. - **Working with Multiple Data Frames**: Efficient data merging techniques. - **Nobel Laureates**: Hands-on lab (for those at that stage). **By the end of this module, you should be able to:** - Clean and reshape data using `tidyverse` tools. - Use `dplyr` for common data wrangling tasks. - Work with multiple datasets efficiently in R. [Back to Menu](#2) --- ## **Module 4: Data Diving with Types** ### **Today’s Plan:** - **Data Types & Recoding**: Understanding data structures. - **Importing Data**: Reading and processing datasets in R. - **Transformations & Cleaning**: Handling missing data and type conversions. - **Visualizing Spatial Data**: Hands-on lab (for those at that stage). **By the end of this module, you should be able to:** - Understand different data types and their implications. - Import and process data from various sources. - Perform essential data transformations in R. [Back to Menu](#2) --- ## **Module 5: Effective Data Visualization** ### **Today’s Plan:** - **Refine & Troubleshoot**: Improve visualizations based on feedback. - **Analyze Misleading Visuals**: Discuss & revise problematic designs. - **Spatial Data Visualization**: Hands-on lab (for those at that stage). **By the end of this module, you should be able to:** - Apply effective `ggplot2` techniques, including appropriate geoms, scales, and coordinate systems. - Analyze misleading visualizations and discuss better design choices. - Use spatial data visualization techniques in the lab. [Back to Menu](#2) --- ## **Module 6: Confounding and Communication** ### **Today’s Plan:** - **Understanding Confounding**: Case studies and discussion. - **Simpson’s Paradox**: Exploring real-world implications. - **Science Communication Strategies**: Effective storytelling with data. - **Ugly Charts**: Hands-on lab (for those at that stage). **By the end of this module, you should be able to:** - Explain confounding and its impact on study design. - Identify and analyze Simpson’s Paradox in real-world datasets. - Apply principles of effective data storytelling. - Critically evaluate misleading or poorly designed data visualizations. [Back to Menu](#2) --- ## **Module 7: Web Scraping** ### **Today’s Plan:** - **Introduction to Web Scraping**: Basics of extracting data from the web. - **Techniques & Tools**: Using R for scraping, including `rvest` and `httr`. - **Top 250 Movies on IMDB**: Exploring a real-world scraping example. - **Hands-on Lab: Better Viz** (for those at that stage). **By the end of this module, you should be able to:** - Explain the fundamentals of web scraping and its applications. - Use R packages like `rvest` and `httr` to scrape data. - Apply CSS selectors using SelectorGadget. - Work with real-world web scraping examples in R. [Back to Menu](#2)